class_docstr: Callback that prints metrics to stdout.
  
  Arguments:
      count_mode: One of "steps" or "samples".
          Whether the progress bar should
          count samples seen or steps (batches) seen.
      stateful_metrics: Iterable of string names of metrics that
          should *not* be averaged over an epoch.
          Metrics in this list will be logged as-is.
          All others will be averaged over time (e.g. loss, etc).
          If not provided, defaults to the `Model`'s metrics.
  
  Raises:
      ValueError: In case of invalid `count_mode`.
code: def _reset_progbar(self, epoch=None):
      self.seen = 0
  
      prevprogbar = self.progbar
      self.progbar = None
      self.progbar = Progbar(
          target=self.target,
          verbose=self.verbose,
          stateful_metrics=self.stateful_metrics,
          unit_name="step" if self.use_steps else "sample",
          epoch=epoch,
      )
      if prevprogbar is not None:
          # inherit the column widths from the previous progbar for nicer looks
          self.progbar._compact_table_column_width = (
              prevprogbar._compact_table_column_width
          )
  
imports: import copy
  import os
  import sys
  import time
  import numpy
  from .callback import Callback
label_desc: Use image node_id 13 for calling the ProgbarLogger obj's underlying member method code with example usage: obj._reset_progbar(epoch) without return types